AGU 2021

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Motivations

Fluvial and tidal channels form in unique environments under varying flow conditions. The main difference between flow conditions is the unidirectional flow and annual flooding in fluvial channels, while tidal channels experience daily bankfull conditions and bi-directional flow. Despite these differences, analysis of planform metrics such as meander wavelength, sinuosity, and mean curvature reveal that values for these metrics overlap significantly between tidal and fluvial channels. In other words, tidal and fluvial channel planforms are remarkably similar. A recent study by Finotello et al. (2020) demonstrated, using a global dataset of ~10,000 individual tidal and fluvial meanders, that the distribution of meander asymmetry indices (i.e. upstream or downstream skewing) differs significantly between tidal and fluvial channels. While this finding does support the conclusions about the effects of flow direction of fluvial channels, the efficacy of asymmetry as a diagnostic tool for classifying individual channel reaches has not thoroughly been tested.

Kolmogorov-Smirnov tests comparing the asymmetry distributions known fluvial and tidal channels in central Baja California, MX and and coastal Iran to the global dataset of Finotello et al. (2020) showed that asymmetry distributions for fluvial and tidal channels in both localities did not match those of their respective global counterparts. While the asymmetry is an important step forward, additional work is needed to identify ways to differentiate between tidal and fluvial channels.

Correctly identifyng tidal and fluvial channels in relict landscapes and in 3D seismic surveys provides robust paleo-sea level indicators. In central Baja California, Mexico there are abandoned channel planforms preserved at elevations up to 450 m above sea level. If these channels are reliably identified as tidal, they also serve as a constraint on paleo-sea level, and as a test for the trans-peninsular seaway hypothesis for within species genetic divergence between populations on the northern and southern parts of the peninsula.

Approach

We propose to develop a classification system for individual channels and channel networks using pre-existing metrics and metrics placed within the the context of along-channel position, rather than isolating the measurements of individual meanders. This is done through quantifying along-channel variations in these metrics using geometric and spectral analysis

1. Channels banks are hand digitized in QGIS
2. Channel banks points are processed in R through cmgo to extract channel center line and channel width at regular intervals along the centerline, using Voronoi polygons and transects.
3. Local-channel curvature extracted using centerline points in MeanderMorph (self-developed software; Python)
4. Curvature signal is processed via continuous wavelet transform to extract multiple loop trees and multi-scale architecture (MeanderScribe; MATLAB)
5. Extract meander metrics for each meander with respect to position along the measured reach.
6. Perform statistical analyses to identify significant differences between metrics.
7. Use significantly different metrics, curvature signals, and width signals to build criteria to identify tidal and fluvial channels
8. Test criteria against dataset of known channel types, refine if needed.
9. Apply to relict channel planforms

Preliminary Results

• Tidal channels display a quasi-exponential increase in channel width
  downstream and non-stationary channel curvature signals.
• Fluvial channels display a linear increase in channel width downstream, and comparatively stationary channel curvature signals
• The example relict channel has a curvature signal similar to that of modern tidal channels, however the width signal suggests a more complicated scenario
  • From 1.0-0.2 along the reach, the channel appears to have quasi-exponential growth in channel width, consistent with modern tidal channels.
  • From 0.2-0.0 along the reach, the channel width decreases linearly, which is inconsistent with both modern tidal and fluvial channels. Could this potentially reflect modern fluvial over printing of a narrower active channel, onto a wider relict tidal channel?

Future work

The work shown in this poster is still in the early stages. We are developing software tools to extract more specific metrics discussed above, and will implement them on a set of modern tidal and fluvial channels that cover a spectrum of tidal ranges (micro- to mega-tidal) and a range of mean discharges (10^1, 10^2, and 10^3 m^3/s) across a range of climates. Once the software tools have been developed and benchmarked, they will be made available through a repository to allow researchers to extract these metrics for any meandering channel.

Additionally this work will be expanded to include analysis of channel networks focused on junctions, stream order, bifurcation angles, and interconnectivity between drainage areas. This will help to identify differences in tidal and fluvial channel networks, which are also remarkably similar.

This project will produces tools to measure and quantify meandering channels and channel networks, and a classification system that can reliably differentiate between channel types. These tools have implications for paleo-sea level reconstructions and the ability to objectively measure meandering channels without needing subjective filtering methods and manual data picking.

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